By making use of this process, translation pairs with low frequencies have relatively high probabilities to be totally discarded, and in resulting TMs, their probabilities could be zero; meanwhile, translation pairs with high frequencies still have high probabilities to be reserved, and hold similar probability feature values in resulting TMs comparing to the main model.

after TMG procedure, feature values could be smoothed for translation pairs with low frequencies, and be stable for translation pairs with high frequencies.


Algorithm 1: Compute n-gram posterior probabilities on hypergraph (Kumar et al., 2009) 1: sort hypernodes topologically 2: compute inside/outside probabilities and for each hypernode 3: compute edge posterior probability for each hyperedge 4: for each hyperedge do 5: merge n-grams on and keep the highest probability when n-grams are duplicated 6: apply the rule of edge to n-grams on and propagate gram prefixes/suffixes to 7: for each n-gram introduced by do 8: if then 9: 10: else 11: 12: end if 13: end for 14: end for 15: return n-gram posterior probability set 2 We omit the similar algorithm for lattices because of their homogenous structures comparing to hypergraphs as we discussed in Section 2.2.

the total gain score for hypothesis on can be further expanded as: where is a mixture ngram posterior probability.


For such senses, no method can provide suitable images, as might be expected.

we exclude targets where it is "difficult to portray the sense using images", then we recalculated the ratio of appropriate images.


Then, with "expanded by minor sense" and when the original sense is dominant majority, expansion reduced the precision.

we should expand using only words with major senses.


However 68.8 % of "not Concrete" nouns are also judged as "Shown".

both POS and type ("Concrete" or "not Concrete") are helpful, but not perfect features as regards knowing the sense is "difficult to portray the sense using images".


Extractive methods also tend to be verbose and this is especially problematic when the summaries need to be viewed on smaller screens like on a PDA.

an informative and concise abstractive summary would be a better solution.


The σ gap parameter controls the maximum allowed gaps in discovering these redundancies.

a common sentence X between nodes v q and v r , will be considered a valid intersect if (P Based on path redundancy, we propose several ways to score a path for the purpose of selecting a good path to include in the summary: i is the first node in the path being scored and v s is the last node.


Our graph representation naturally ensures the coherence of a summary, but such a graph emphasizes too much on the surface order of words.

it cannot group sentences at a deep semantic level.


A conventional generative word aligner does not support this type of constraints.

we developed a constrained EM algorithm that can use the links from a partial alignment as constraints and estimate the model parameters by marginalizing likelihoods.


Target word misalignment: The IBM models assume that one target word can only be aligned to one source word.

if the target word f j aligns to a source word e i , while the constraint α J I suggests f j should be aligned to e i , the alignment violates the constraint and thus is considered inconsistent.


(2009) experimented with different alignments produced by adjusting the filtering threshold for the alignment links and showed that they could get high-precision-low-recall alignments by having a higher threshold.

we replicated the confidence filtering procedures to produce the partial alignment constraints.


many queries contain valid search terms, such as proper nouns and names, which are not well established in the language.


As a result, many queries contain valid search terms, such as proper nouns and names, which are not well established in the language.

recent research has focused on the use of Web corpora and search logs, rather than human-compiled lexicons, to infer knowledge about spellings and word usages in search queries (e.g., Whitelaw et al., 2009;Cucerzan and Brill, 2004).


Zhang et al., 2006;Brants et al., 2007), it is also clear that search queries are composed in a language style different from that of the body or title of a Web document.

in this study we developed a set of large LMs from different text streams of Web documents and query logs.


Once the word alignment is fixed, the final permutation is uniquely determined, so we can safely discard that factor.

we have: For the sole remaining factor P(T|C, S), we make the assumption that a segmented query T = q 1 … q K is generated from left to right by transforming each phrase c 1 …c K independently: where is a phrase transformation probability, the estimation of which will be described in Section 5.2.


One possible reason is that our baseline system, which does not use any error model learned from the session data, is already able to correct these basic, obvious spelling mistakes.

including these data for training is unlikely to bring any further improvement.


Inserting another tree into this initial tree satisfies one of the substitution or adjunction requirements, but introduces some new requirements into the resulting tree, in the form of its own substitution nodes and adjunction sites.

intuitively, the RTG representation of a SEMXTAG elementary tree is a rule that rewrites the satisfied requirement as a local tree whose root is a unique identifier of the tree and whose leaves are the introduced requirements.


In this configuration, all the feature structure information present in the SEMXTAG elementary trees is carried over to the RTG rules.

tree combining and constraint solving proceed simultaneously and the generated parse forest contains the derivation trees of all the output sentences.


Our reorderings are constrained by reordering of nodes in a parse tree of the source sentence.

the full space of reorderings we consider consists of all reorderings that would produce a parse tree with the same set of child-parent relationships.


For example, the Rand Index will always be 1.0 when each data point belongs to its own cluster.

we have chosen to calculate metrics other than the Rand index: cluster precision, recall, and f-score.


While English and Persian are related, they have been diverging in geographically distant areas for thousands of years.

the fact that English appears to share more features with a geographically closer Finnish is expected.


Also, some features in WALS overlap; for example, one WALS feature looks at the order between subject, verb, and object, and another feature checks the order between verb and object.

the feature set in WALS might not be a good representative of the properties of the languages covered in the database.


In the ATB, asta'adah is tagged 48 times as a noun and 9 times as verbal noun.

all three parsers prefer the nominal reading.


But we follow the more direct adaptation of Evalb suggested by Tsarfaty (2006), who viewed exact segmentation as the ultimate goal.

we only score guess/gold pairs with identical character yields, a condition that allows us to measure parsing, tagging, and segmentation accuracy by ignoring whitespace.


Many PDF files were not papers and many others were converted to gibberish files.

the examined corpus contains only 10,100 papers.


Then we choose some complicated reordering models to translate those phrases.

search space and computational complexity are both reduced.


Three phrase tables: chunk phrase table, sub-sentence phrase table and base phrase table are merged together and fed into the monotone decoder.

the decoder will automatically choose those phrases it need.


A synset is a set of words (called lexical units) where all the words are taken to have (almost) the same meaning.

a synset is a set-representation of the semantic relation of synonymy, which means that it consists of a list of lexical units and a paraphrase (represented as a string).


Each Synset has a reference to the Ger-maNet database.

the Monolin-gualExternalRef element links to the corresponding entry in the GermaNet database; the externalReference attribute specifies the database table name with the synsets database ID.


In the current Wordnet-LMF DTD only conceptual relations (i.e., SynsetRelations in the terminology of Wordnet-LMF), which hold between synsets, are modeled.

antonymy, which is a typical example of a lexical relation (see (Fellbaum, 1998) for further details), can cur-rently not be modeled without violating the Wordnet-LMF DTD.


They can be used in statistical machine translation (SMT), cross language information retrieval, and paraphrasing.

the acquisition of bilingual corpora has received much attention.


to search for TL documents among the Web, using the search engines of Google and Yahoo.

we are subject to a much noisier data domain.


It is assumed that a machine translation (MT) system is better at handling lexical ambiguity than simple dictionary translation.

we propose to first translate the SL document into TL and extract the top-N TF-IDF-ranked words as query.


In fact, they do not only satisfy the requirement of length and cue phrases (described in section 2), but they also have another property that they are translated from some English news articles (henceforth target pages) on the Web.

they are ideal data for studying the performance of document pair retrieval.


Subjective evaluation on selected samples shows that most of the document pairs kept by the filter are genuinely parallel.

the document pairs can be broken down into sentence pairs simply by a sentence alignment method.


These relation features are however very accurate when extracted and are present for prediction.

they are strong disambiguation evidence and their removal would significantly hamper performance.


We observed that websites tend to dedicate a url per entity.

grouping by click urls can lead to clusters with synonyms (i.e., different ways of representing the same entity) or variants (e.g., spelling errors).


However, since the boundaries of the spans annotated in the MPQA corpus are not strictly defined in the annotation guidelines (Wiebe et al., 2005), measuring precision and recall using exact boundary scoring will result in figures that are too low to be indicative of the usefulness of the system.

most work using this corpus instead use overlap-based precision and recall measures, where a span is counted as correctly detected if it overlaps with a span in the gold standard (Choi et al., 2006;Breck et al., 2007).


Below the Word level, phonemes can either be treated as independent entities, or else they can be grouped into onset, nuclei and coda clusters, and these can vary depending on where they appear within a word.

there are three different values for the syllable dimension, so there are twelve different adaptor grammars overall.


Here, we do not need to consider the number of terminals outside the span of C (to the left or right or in the gaps), they are not relevant for the inside probability.


Additionally, when comparing this to PCFG parsing results, one has to keep in mind that LCFRS parse trees contain non-context-free information about discontinuities.

a correct parse with our grammar is actually better than a correct CFG parse, evaluated with respect to a transformation of Ne-Gra into a context-free treebank where precisely this information gets lost.


Already in this first attempt with a straightforward binarization, we obtain results that are comparable to state-of-the-art PCFG results in terms of F 1 , while yielding parse trees that are richer than context-free trees since they describe discontinuities.

our approach demonstrates convincingly that PLCFRS is a natural and tractable alternative for data-driven parsing which takes non-local dependencies into consideration.


Many times, a document is found to be unreadable due to unusual linguistic constructs or ungram-matical language that tend to manifest themselves in the syntactic properties of the text.

syntactic features have been previously used (Bernth, 1997) to gauge the "clarity" of written text, with the goal of helping writers improve their writing skills.


This typically happens in cases when sentences are ungrammatical, and possibly, less readable.

we use the proportion of such incomplete parses within a document as a readability feature.


Out-of-vocabulary (OOV) rates: We conjecture that documents containing typographical errors (e.g., for closed-caption and web log documents) may receive low readability ratings.

we compute the OOV rates of a document with respect to the various LMs shown in Table 1.


As shown in the following section, ablating features affects predictive accuracy much more than changing the regression algorithm.

on this task, the choice of regression algorithm was not very critical once good readability features are used.


Since the genre-based features exploit knowledge of the genres of text used in the MRP readability corpus, their utility is somewhat tailored to this specific corpus.

it is useful to evaluate the performance of the system when genre information is not exploited.


Training instances of these words are then injected into the training data and the parameters learnt from them are used instead of the projected parameters learnt from the source language corpus.

the selective sampling strategy ensures that we get maximum value for money by spending it on annotating only those words which would otherwise not have been disambiguated correctly.


Further, in Table 7 we see that when PCL is used in conjunction with certain amount of annotated corpus we get up to 9% improvement in F-score as compared to the case when the same amount of annotated corpus is used alone.

in the absence of skilled bilingual lexicographers, PCL can still be used to boost the accuracy obtained using annotated corpora.


For example, when an annotated corpus of size 2K is used, selective sampling gives an advantage of 3% to 4% over random selection.

the marginal gain (i.e., value for money) obtained by using selective sampling is more than that obtained by using random annotation.


If there are one or more gaps in the word sequence in L2 aligned with an entity mention in the sentence in L1, we assume that the corresponding alignments are likely to be erroneous.

the alignments of non-contiguous words are excluded in projection.


The reliabilities of instances projected via a series of independent modules are different from each other.

we propose an assessment strategy for each projected instance.


For example, some keyphrases are often partly or wholly subsumed by other candidates or may not even occur in the document.

counting the exactly-matching candidates has been shown to be suboptimal (Jarmasz and Barriere, 2004).


For example, AB and BC from ABC would be scored as 11 , respectively.

with the keyphrase effective grid computing algorithm and candidates effective grid, grid computing and computing algorithm, modified R-precision assigns different scores for each candidate (computing algorithm > grid computing > effective grid).


all the ACE mentions are already known from the annotated corpus).

the actual effect of this joint anaphoricity-coreference ILP formulation on fully automatic coreference resolution is still unclear.


For the example sentence, there are two predicates "said" and "bit", which head the "VP" and "S" nodes in the root path re-spectively.

these two predicates and their corresponding paths should be attached to the root path as shown in Figure 1(e).


Our approach is completely useless in this situation: it only generates new information for target or source phrases that are already in the system's phrase table.

we find paraphrases for many of the source and target phrases that are in the phrase table, while the work cited above looks for paraphrases of source phrases that are not in the phrase table.


We defined the distance as that between the two phrases with the most observations in each cluster.

distance between clusters 1 and 2 would be Edit("red", "dark burgundy")=1.0.


As Table 5 shows, cosine has slightly better Top 1 figures, while Dice is a little better when considering the Top 20 translation candidates.

as previously mentioned, the choice of similarity measure (cosine or Dice) should depend on the goal of translation spotting.


A closer look at the translation candidates obtained when using LL, the most popular association measure in projection-based approaches, shows that they are often collocates of the reference translation.

ll may fare better in an indirect approach, like the one in (Daille and Morin, 2005).


1 1 These CSG rules apply to trees of depth two or more, and the applications are dependent on surrounding contexts.

they are different from CFG rules which apply only to trees of depth one, and TSG (tree substitution grammar) rules for which variables are independently substituted by substitution.


Thirdly, apart from negation, many other phenomena, e.g., contrast transition with trigger words like 'but', 'however', and 'nevertheless', can shift the sentimental polarity of a phrase or sentence.

considering negation alone is inadequate to deal with the polarity shifting problem, especially for document-level sentiment classification.


The basic idea of automatically generating the polarity shifting training data is based on the assumption that the real polarity of a word or phrase is decided by the major polarity category where the word or phrase appears more often.

the sentences in the frequently-occurring category would be seen as polarity-unshifted while the sentences in the infrequently-occurring category would be seen as polarity-shifted.


Apart from that, the first word in the following sentence is also included to capture a common kind of long-distance polarity shifting structure: contrast transition.

important trigger words like 'however' and 'but' may be considered.


For French, all the articles with a depth less than 7 below the category Société are extracted.

the English corpus Wiki-En consists of 367,918 documents and the French one Wiki-Fr consists of 378,297 documents.


It can provide deeper syntactic dependencies for object features, positive opinions and negative opinions.

it is important to consider the syntactic structure in the review mining task.


In each iteration, we ran 4 passes of clustering using different initial values.

in total there are 56 sentence clusters generated in each S D F .


We wanted to check if such a corpus is essential for the document-centric method, and if the need for pre-selection can be reduced through filtered ranking.

we set up another experiment to see if the document-centric method is stable or sensitive to different corpora.


The reason might be as follows: Die events appear frequently in ACE 2005, and most instances correspond to a small number of forms, so it is easy to find the correct patterns both from WordNet or related documents.

filtered ranking provides no apparent benefit.


For example, of the sentences which contain die events, 29% also contain attack events.

in word level evaluation, filtered ranking continues to outperform either document-or similarity-centric methods, and its advantage over document-centric methods is accentuated.


For instance, using the rule (4) in Section 2.1 to generate an item leads to the following deductive step: As PP-VPB is a virtual non-terminal, the subtree it dominates is a virtual tree, for which we cannot figure out its PCFG probability.

we have to postpone the calculation of PCFG probabilities until reaching a natural non-terminal such as IP.


Although widely used in natural language processing, PCFGs are often criticized for the lack of lexicalization, which is very important to capture the lexical dependencies between words.

we use Collins' Model 1 (Collins, 2003), a simple and effective lexicalized parsing model, as the second parsing feature in our decoder.


We binarized 99.6% of tree-to-string rules into 16.2M SCFG rules and discarded non-binarizable rules.

the search space of our decoder is much larger than those of the tree parsing counterparts.


Conditional Entropy-based: Aspects from a structured ontology are generally quite meaningful, but they are not designed specifically for organizing the opinions in our data set.

they do not necessarily correspond well to the natural clusters in scattered opinions.


It is easy to prove that the problem is NP-complete.

we resort to greedy algorithms to find approximations of the solution.


It provides flexibility of applying the methods in different domains without the requirement of training data, benefiting from both the ontology based template guidance as well as data-driven approaches.

we can generate different results for different topics even in the same domain.


It is also very hard for human to read all of the aspects and opinions and then select a gold standard subset.

we opt to use indirect measures capturing different characteristics of the aspect selection problem (1) Aspect Coverage (AC): we first assign each aspect A i to the cluster C j that has the most overlapping sentences with A i , approximating the cluster that would come into mind when a reader sees A i .


While this approach works when translating between languages with limited morphology such as English and French, it has been found inadequate for morphologicallyrich languages like Arabic, Czech and Finnish (Lee, 2004;Goldwater and McClosky, 2005;Yang and Kirchhoff, 2006).

a line of SMT research has worked to incorporate morphological analysis to gain access to information encoded within individual words.


of identical preceding/following words in source and target word contexts Morphology root-subsumption string indicate if source root equals, has as prefix, is a prefix of, has a suffix, is a suffix of, has as infix or is an infix of target root roots-share-pre-/in-/suffix bool source and target root share a prefix/infix/suffix  esting to observe that MBGM obtains higher Fscores on Equals and on Intersects (the two most frequent relations) than the human annotators obtained.

of this, the micro F-score of the automatic full tree alignment is less than 2% lower than the human reference score.


POMDPs need evaluation scores (i.e., d) for dialogue acts (i.e., a) for training a reward function.

we asked a third-party participant, who was neither a listener nor a speaker in our dialogue data collection, to evaluate the user satisfaction levels of the collected dialogues.


This is because greetings appear in all dialogues and their user satisfaction reward converges to the average.

such actions as greetings do not get repeated in POMDP.


In our qualitative analysis, we observed that randomness in dialogues might hold a clue for improving evaluation scores.

we measured the perplexity of each system output using dialogue-act trigrams and obtained 72.8 for "Random," 27.4 for "Human dialogue," 7.4 for "POMDP," 3.2 for "HMM," 2.5 for "Even-POMDP," and 1.7 for "Rule-based."


Yet the texts in the task were manually modified to ensure they are self-contained.

little effort was invested by the challenges' participants to address discourse issues beyond the standard substitution of coreferring nominal phrases, using publicly available tools such as JavaRap (Qiu et al., 2004) or OpenNLP 1 , e.g.


The document's top-n global terms are added to each of its sentences.

a global term that occurs in the hypothesis is matched in each sentence of the document, regardless of whether the term explicitly appears in the sentence.


Title entailment In many texts, and in news articles in particular, the title and the first few sentences often represent the entire document's content.

knowing whether these sentences entail the hypothesis may be an indicator to the general potential of the document to include entailing sentences.


However, it is not trivial to find pairs of opposite emotions in the case of a fine-grained classification, except for 'joy' and 'sadness'.

we assume that (1) the opposite emotion for three positive emotions, i.e.


4 The morphological disambiguator segments a source word to morphemes of smallest meaning-bearing units of the source language.

a target word is equivalent to one or several morphemes.


The first one represents a node in a data structure (STACK or QU EU E) and an attribute: The nodes are identified using a zero-based index.

sTACK 1 designates the second node on the stack.


Another category where Arg errors are frequent is RNR, where all such errors consist in attaching the relevant dependent to the second conjunct instead of to the first.

6 in the example in Figure 1f, both parsers found the conj relation between puzzled and angered but attached by to the second verb.


Uses of What as a determiner were consistently mistagged as pronouns, which led to errors in parsing.

for the example in Figure 1e, both parsers produced the correct analysis except that, because of the tagging error, they treated What rather than effect as the head of the wh-phrase, which counts as an error in the evaluation.


This is true even though MSTParser and MaltParser have not been engineered specifically for English and lack special mechanisms for handling unbounded dependencies, beyond the simple post-processing heuristic used to extract them from the output trees.

it is reasonable to speculate that the addition of such mechanisms could lead to computationally lightweight parsers with the ability to extract unbounded dependencies with high accuracy.


From these comparison, we can see that virtual-common instances coupled with genuinecommon instances can be leveraged to enable more effective collaboration between the two classifiers in Co-STAR.

our proposed method outperforms the others in F-measure by 1.4-8.5%.


Presuming that we are searching for strings of |Y | = 16, we obtain the necessary and sufficient condition for the approximate dictionary matching from the inequality 3, τ = 12 ≤ |X ∩ Y |.

we need to search for strings that have at least 12 letter trigrams that overlap with X.


For example, to obtain candidate strings in the IMDB dataset, the proposed algorithm scanned 279.7 strings, whereas the AllScan algorithm scanned 16,155.1 strings.

the algorithm examined only 1.1%-3.5% of the strings in the entire inverted lists in the three datasets.


Eye gaze, in particular, has often been implicated as eliciting listener feedback.

we manually annotate the following contextual feature: speaker looking at the listener.


Our goal was to find the "true" British spellings that conformed to a British viewpoint, but American spellings predominate by far.

without prior knowledge the fact-finders do very poorly against our test set of 694 British words, predicting American spelling instead in accordance with the great majority of authors (note that accuracy from an American perspective is 1−"British" accuracy).


It has been shown before in (Kulik et al., 2009) that if f is a submodular, nondecreasing set function and f (∅) = 0, then such a greedy algorithm finds a set S, whose gain is at least as high as (1 − 1/e) of the best possible solution.

we can optimize the keyphrase coverage as described in Algorithm 1.


size of words) is much larger than standard double array Trie, which has only 256 children, i.e, range of a byte.

inserting a 2D Trie node is more strict, yielding sparser double arrays.


derived from the scores of opinions in a document.

our method has a sound statistical foundation and can be applied to arbitrary reviews with mixed opinion polarities and strengths.


But our system give a result of "00100000" because the emotion holder " (daughter)" of the emotion word " (interest)" has not been considered in our algorithm.

the recognition of emotion holder is indispensable for an accurate emotion analysis system.


As we want to compare time requirements needed for annotating random samples and sentences selected by active learning, we had to control for training effects which might speed up the annotation.

we changed the annotation setting after each package, meaning that the first annotator started with 50 sentences randomly selected from the pool, then annotated 50 sentences selected by AL, followed by another 50 randomly chosen sentences, and so on.


The classifier, however, proved to have serious problems learning this particular word sense and thus repeatedly selected more instances of this frame for annotation.

the distribution of word senses in the training set for the uncertainty samples is highly skewed, having a negative effect on the overall classifier performance.


The high percentage of instances of the "easy-todecide" frame Operating result explains why the instances for Ergebnis took less time to annotate in the AL setting.

we can conclude that annotating the same number of instances on average takes more time in the AL setting, and that this effect is not due to sentence length.


Critical features which seem to influcence the outcome of AL are the amount of noise in the data as well as the distribution of frames in training-and test sets.

addressing the class imbalance problem is crucial for applying AL to a real annotation task.


The results indicate small but consistent improvements with increasing data sizes, however, the differences are minimal.

we are not able to conclude at this point whether performance increases are possible with the addition of more data.


Second, when weighing concepts in a multiple-domain corpus, the relevance weights assigned to the concepts do not indicate the significance of each concept in a certain domain.

some core domain specific concepts may have low weights with respect to the whole corpus.


target concepts that are associated with the same source concept should appear in similar lexico-syntactic environments.

clustering concepts using grammatical relations (GRs) and lexical features would allow us to capture their relatedness by association and harvest a large number of metaphorical expressions beyond our seed set.


We considered the expressions tagged as metaphorical by at least three annotators to be correct.

our system identifies metaphor with the precision of 0.79, whereas the baseline only attains 0.44.


Thus in engasesendlini '(s)he no longer being in the house', the initial prefix e-indicates third person singular, class 1, participial mood; the prefix nga-denotes negative; the first prefix se-denotes continuative aspect; the second prefix se-is the locative prefix; n-shows that the noun belongs to class 9; dl-is the noun root meaning 'house', an allomorph of the canonical form -dlu; and -ini is the locative suffix.

in typical agglutinative manner, each affix contributes a distinctive part of the meaning of the word as a whole.


The labels have been chosen to be both as brief as possible and as transparent as possible, though transparency was often sacrificed for brevity.

indicative subject prefixes are labeled <i1-15>, relative prefixes are labeled <r>, and noun prefixes are labeled <n1-15>; but negative subject prefixes are labeled <g1-15> and possessive agreement prefixes are labeled <z1-15>.


On the other hand, the same one-to-one matching restrictions penalize proposed analyses that do not conflate allomorphs of the same underlying morpheme, whether those allomorphs are phonologi-cally induced or not.

eMMA meets our linguistic criteria from Section 1 of modeling syncretism, allomorphy, and morphophonology.


Precision is the fraction of correctly relabeled proposed morphemes from among all proposed morphemes of w k ; while recall is the number of correctly relabeled morphemes as a fraction of the answer key analysis of w k .

precision and recall of the full vocabulary are the average word-level precision and recall: Finally, f-measure is the harmonic mean of precision and recall: 2.3 Morphological Ambiguity in EMMA far we have presented EMMA for the scenario where each word has a single morphological analysis.


Similar to lexical features, we also introduce internal-external word alignment features as follows: where { In addition to the lexical correspondence, we also capture the structural divergence by introducing the tree structural features as follows: Span difference: Translational equivalent subtree pairs tend to share similar length of spans.

the model will penalize the candidate subtree pairs with largely different length of spans.


This is common, since in a compound sentence in English, the entities appeared more than once are often replaced by pronouns at its later appearances.

the syntactic rules constraint by NR 1 -NNP 1 , IP 2 -VP 2 and PP 3 -VP 3 respectively cannot be extracted for syntax systems; while for phrase systems, context around the first "以色列" cannot be fully explored.


In this work we stack stacked dependency parsers by training classifiers on the output of three stacked dependency parsers and POS tags.


To speed annotation, NP internal structure is often left underspecified in PCTB (Xue et al., 2005), as in the Penn Treebank.

68% of non-trace NPs in PCTB have only a flat bracketing.


One important advantage of dependency grammars is that they directly capture the dependencies between words, which are key to resolving most parsing ambiguities.

incorporating dependency trees proves to be effective in improving statistical machine translation (Quirk et al., 2005;Ding and Palmer, 2005;Shen et al., 2008).


To assign a probability to each hyperedge, we can first obtain a positive number for a hyperedge using the scores of the corresponding edges: 1 c(e) = exp v∈tails(e) s v, head(e) |tails(e)| where c(e) is the count of a hyperedge e, head(e) is a head, tails(e) is a set of dependants of the head, v is one dependant, and s(v, head(e)) is the score of an edge from v to head(e).

for example, the count of the hyperedge e 1 in figure 3(c) is Then, the probability of a hyperedge can be obtained by normalizing the count among all hyperedges with the same head collected from a training corpus: c(e) e :head(e )=head(e) c(e ) we obtain a weighted dependency forest in which each hyperedge has a probability.


Although there are exponentially many composed rules, the number of minimal rules extracted from each node is rather limited (e.g., one or zero).

one can obtain promising composed rules by combining minimal rules.


(2007), which binarize each constituent node to create some intermediate nodes that correspond to the floating structures.

we can find k-best fixed and floating structures for a node in a dependency forest by manipulating the fixed structures of its dependants.


First, their meaning is not totally compositional, that is, it cannot be computed on the basis of the meanings of the parts of the collocation and the way they are related to each other.

the result of translating the parts of the collocation can hardly be considered as the proper translation of the original expression.


hunmorph for Hungarian (Trón et al., 2005)), and the combination of a possible light verb and a deverbal noun typically results in a light verb construction.

an algorithm that makes use of morphosyntactic and derivational information and previously given lists can be constructed to identify light verb constructions in texts.


However, the identification of the syntactic status of the nominal component is more complex in the case of light verb constructions for it is a quasi-argument of the verb not to be confused with other arguments (Alonso Ramos, 1998).

the parser should recognize the special status of the quasi-argument and treat it in a specific way as in the following sentences, one of which contains a light verb construction while the other one a verbal counterpart of the construction: Pete made a decision on his future.


Our model assigns non-zero probabilities to trees that can be obtained by permuting the child nodes at various interior nodes of the tree S. We assume that children of a node are ordered independently of all other nodes in the tree.

where I(S) is the set of interior nodes in the tree S, c n is the list of children of node n and π is a permutation.


As in these examples, market information can often only be found in running text; the major source for this is the Web.

we seek to find web pages with sentences that represent statements on a financial criterion χ and to make these statements processable.


In market analysis, the approximate t d suffices, and for most news articles t d is similar to their release date.

if no date is in the parse tree, we search the extracted temporal entities for the release date, which is often mentioned at the beginning or end of the document's content.


The above assumption is reasonable because the documents in the set are relevant and the globally important information in the document set will be expressed in many single documents.

if a sentence is salient in the whole document set, the sentence may be salient in a particular document in the set.


The above assumption is reasonable because the documents in the set are relevant and the globally important information in the whole set is the aggregation of the locally important information in each single document.

if a sentence is salient in a particular document, the sentence has the potential to be salient in the whole document set.


To date, automatic countermeasures mainly involve rulebased approaches and these are not very effective.

wikipedia volunteers have to spend a large amount of time identifying vandalized articles manually, rather than spending time contributing content to the articles.


However they suffer from fixed-size knowledge bases and use only rigid rules.

their average recall is not very high and they can be easily fooled by unseen vandalism patterns.


It is not easy to capture these cases by solely relying on lexical knowledge or rule-based dictionaries and it is also very expensive to update dictionaries and rules manually.

we think that is crucial to incorporate more syntactic cues in the feature set in order to improve performance.


Among various combining methods, log-linear interpolation combination is a simple but effective one (Bishop, 2006).

the following character-based joint model is proposed, and a parameter α is used to weight the generative model in a cross-validation set.


However, our joint-plus model exceeds it more over AS and CITYU corpora by 1.0% spectively (1.0% and 0.3% for the joint model).

it is fair to say that both our joint model and joint-plus model are superior to the stateof-the-art systems reported in the literature.


In contrast, for the pair forest and woods, it might be easy to find words satisfying one but not both constraints.

the lack of such qualifying words -or at least the relative difficulty for finding one -makes the difference between this pair more subtle than in the previous examples.


For example, the word " high"has a positive orientation when the target noun is " salary" but a negative orientation when the target noun is " price" .

the task can be reduced to sentiment classification of nouns.


On the contrary, most sentiment characters derived from BaiduHit have noun property.

the combination of the two lexicons can cover more characters.


More importantly, a tokenization close to a gold standard does not necessarily leads to better translation quality (Chang et al., 2008;Zhang et al., 2008).

it is necessary to offer more tokenizations to SMT systems to alleviate the tokenization error propagation problem.


In Figure2(c), tao fei ke is recognized as a Chinese person name with the second name tao and the first name fei-ke, but the whole string tao fei ke should be a name of the Indonesian badminton player.


Here, glue rule is used to concatenate the translation and tokenization of the two variables S and X, which acts the role of the operator in equation 5.


The certainty of the NP 'supervisors' is 12/13=92.3% and the certainty of the NP 'tasks' is 1/13=7.7%.

at an ambiguity threshold of, for instance, τ = 0.8, the ambiguity in Table 2 is innocuous because the agreement between the judges exceeds the threshold.


slide in the sense of "playground equipment"), though the contextual features that could be used to identify this word sense are not located in the immediately preceding sentences.

in contrast to supervised, local phrase sense disambiguation employed in previous work, we propose to utilize unsupervised, global word sense disambiguation, in order to obtain better modeling of the topic and domain knowledge that is implicitly present in meeting conversations.


This indicates that the choice of surface sentence form is not entirely random, but conditioned by some factors including information status.

instead of categorizing sentences as grammatical or ungrammatical, a better way to express the degree of grammaticality would be to use a likelihood continuum, from 0 to 1, where ungrammatical sentences have zero likelihood and grammatical sentences fall somewhere between 0 and 1, with some being more likely than others.


In 5a, the NP women "we" is both the recipient of the first verb song "to send" and the agent of the second verb chi "to eat".

by using a pre-IO form, the NP women is in effect adjacent to the second verb chi, which might give an advantage in sentence processing.


Though the part of variation it accounts for can also be explained by other predictors, it is more effective in doing so.

the existence of this variable tends to mask other predictors in the model.


pronouns, definite NPs, NPs with antecedents) tend to occur earlier in the construction.

in the lower model, the general trend of prominence is confluent with the short before long weight effect, but in the upper model, it is pulling away from the long before short weight effect.


Such similar contexts may decrease classifiers' ability to discriminate among near-synonyms.

we propose the use of a supervised discriminative training technique (Ohler et al., 1999;Kuo and Lee, 2003;Zhou and He, 2009) to improve classifiers by distinguishing positive and negative features for each near-synonym.


Similarly, ( ) is the number of times x occurs in the corpus, and ( ) ( ) P y C y N = , where C(y) is the number of times y occurs in the corpus.

(1) can be re-written as Inkpen (2007) computed the PMI scores for each near-synonym using the Waterloo terabyte corpus and a context window of size 2k (k=2).


The training set size is increased, which helps produce better results as our experimental results show.

in the first iteration of EM, soft-labeled examples SL are treated in the same way as the labeled examples in L. both SL and L are used as labeled examples to learn the initial classifier f 0 .


We can also see it as clustering with initial seeds.

we also use clustering evaluation methods.


For example, the supertag predictions for some words can contribute to high supertagging accuracy, but cause the parser to fail.

considering the global grammar constraints in the supertagging training stage can make the supertagger and the Algorithm 1: Forest-guided supertagger training Input: Generate supertag lattice using the point-wise classifier with current θ 5: Selectŝ i from the lattice which can construct a tree with largest sequence score 6: θ sum ← θ sum + θ i+1 Return: θ sum /N T parser more tightly related, which will contribute towards the performance of the parser.


Articles in Wikipedia often contain mentions of entities that already have a corresponding article, and at least the first occurrence of the mentions of an entity in a Wikipedia article must be linked to its corresponding Wikipedia article, if such an article exists.

if the mention is ambiguous, the hyperlink is disambiguating it.


(2009) illustrated that noun phrase and n-grams longer than 2 were not effective in comparison with tokenbased features and using bi-grams gives the best results only reaching recall 0.7.


For example, there are quite a few paraphrases concerning the therapy of various diseases, from which we can easily induce patterns expressing the meaning of "How to treat [X] disease", such as "[X] 病 如 何 治疗", "怎么 治疗 [X] 病", and "[X] 病 的 治疗 方法".

in this work, we try to generate paraphrase patterns using the extracted paraphrases.


However, we have not found clear improvement after observing the results.

we do not adopt the augmentation strategy in our experiments.


A paraphrase that is totally transformed from the source sentence and another that is almost unchanged may get the same score.

we propose two strategies, i.e., PR1 and PR2, to compute the paraphrase rate: Here, PR1 is defined based on word overlapping rate, in which OL(S, T ) denotes the number of overlapping words between a paraphrase T and its source sentence S, L(S) denotes the number of words in S. PR2 is defined based on edit distance, in which ED(S, T ) denotes the edit distance between T and S. Obviously, PR1 only measures the percentage of words that are changed from S to T , whereas PR2 further takes word order changes into consideration.


• Web search engine queries resulting in user clicks on a Wikipedia article are taken as alternative surface forms for the entry.

we get a number of surface forms for each entry e i .


The basic idea is that, although there are annotation divergences between heterogenous treebanks, actually we can also find consensus in annotations of bracket structures.

we would like to train parsers on individual heterogeneous treebanks and guide the parsers to gain output with consensus in bracket structures as much as possible when they are parsing the same sentences.


Moreover, the hypotheses 3 spanning the same range of words are readily stacked together in a chart cell before CKYstyle decoders move on to process other spans.

member decoders can process the same word spans collaboratively from small ones to big ones until they finally complete parsing the entire sentence.


5 shows the bracketing F1 on the CTB test set at different settings of the best-first cache size C. F1 scores reach the peak before C increases to 6.


When we apply phrase substitution on a non-terminal node, then any simplification operation (including dropping, reordering and substitution) cannot happen on its descendants any more because when a node has been replaced then its descendants are no longer existing.

for each non-terminal node we must decide whether a substitution should take place at this node or at its descendants.


5 shows one of the possible substitution results for our example where "ancient" is replaced by "old".

figure 5: Substitution of all the simplification operations, we obtain the following two sentences: s1 = Str(pt1)="August was the sixth month in the old calendar."


we have three probabilities p 1 , p 2 , p 3 for one argument.


The combination process can also be seen as a function, which maps the outputs and weights to the combined result r: r = Comb(O n 1 , w n 1 ).

the error function of our system on development set is: From equation 5, it can be seen that: Given development set d, if the outputs to be combined O n 1 and the combination method Comb are fixed, the error function is just a function of the weights.


That is to say, with MEW strategy, the benefit of combination is improved by about 20%.


Since the evaluation starts with disfluencies found by the model, this measurement has precision-like properties only.

there are easy ways to inflate the score artificially at the cost of recall.


Similarly, for example (2), the descriptions of typical murmurs associated with hypertrophic cardiomyopathy (harsh, systolic, and diamond-shaped) may not fall under the same section as the impact of Valsalva maneuver on the murmur (which is a factor used to distinguish hypertrophic cardiomyopathy from aortic stenosis).

a typical passage retrieved from most reference corpus would cover only a portion of the facts given in the question.


Each two dimensional matrix, M in M 3D belongs one passage scorer.

depth of M 3D is the number of passage scorers used to match supporting passages with the question.


3 We ask non-expert workers to provide annotations for POV, a difficult decision for a subset of sentences.

the design of the HIT (Human Intelligence Task) in AMT is crucial: in order to get acceptable agreement, the AMT task must be simple and easy to understand; definitions must be clear and the annotation interface well-structured.


He shows that a term often cannot be aligned to any term in the target language.

he proposes that specialised translation dictionaries should store different translation possibilities or term variants.


Since the UK GAAP is a monolingual ontology, it holds no reference translation needed for automatic evaluation.

we performed several experiments to find the best approach to translate this financial ontology.


Since we built a financial parallel resource (see Section 3.4 and 3.5) and generated a translation model based on this financial vocabulary, we tested how well the model performs on a similar ontology.

we translated the aforementioned German GAAP ontology, which holds 2,794 labels 15 .


All these sub-experiments proved that the approach of building new, specific resources showed a large impact on the translation quality.

generating specialised resources for different specific domains will be the focus of our future work.


(1) Basque is a morphological ergative language (Dixon, 1994) as well in both case-marking and verbal auxiliary morphology.

case-marking for subjects of intransitive verbs and objects of absolut i ve = sub jec t o f int r ansi t i ve ver bs o b jec t o f t r anst i ve ver bs er g at ive = sub jec t o f t r ansi t i ve ver bs Figure 1: Two cases in Basque, and their respective syntactic functions depending on the transitivity of verb.


Work in parse correction consists on the creation of corrective models to solve difficult ambiguities, such as PP-attachment ambiguity in English.

correction occurs as a post-process to parsing, replacing the output of the parser (labels or attachments) with alternatives obtained in an independent classification process.


extensions to feature set 2 are considered for further experimentation.


Although our method uses no reference translations, it still maintains the notion of MT evaluation.

in lack of openly available competitors of its kind, it makes sense to compare its performance with automatic state-of-the-art MT metrics, to whom reference translations get available.


Adding a lot of out-of-domain data to an in-domain SMT system tends to overwhelm the in-domain characteristics, thereby negatively affecting translation quality.

relevant data selection from large supplementary out-of-domain data plays an important part in domain adaptation of SMT systems.


Since the batches are ordered according to perplexity-based similarity with respect to the target domain the algorithm makes it increasingly harder for a batch to get into the final selection as (i) later batches are less similar to the targeted domain and (ii) they need to improve on a steadily improving baseline.

the algorithm implements the intuition that only those parts of generic supplementary data are selected which are good enough to generate better translation quality on the devset.


Word alignment estimation is the most computationally expensive process in TrM training.

in order to avoid re-estimation of word-alignments in every iteration, we once and for all pre-compute the word alignments on the entire supplementary dataset and use this in every iteration.


Stopword n-grams (Stamatatos, 2011) are based on the idea that text reuse often preserves syntactic similarities while exchanging content words.

the measure removes all content words while preserving only stopwords.


However, for text reuse detection it is important to have aligned pairs of reused texts and source texts.


It turns out, though, that the reused text is about four times as long as the original press agency source, with lots of new facts being introduced there.

only a low similarity score  can be computed between the additional material in the newspaper article and the original source, and the overall similarity score decreases.


Comparing the results to the results retrieved by other works is difficult because there is neither a shared task for paraphrase extraction nor common resources for comparison.

we show some manual evaluations of our results.


When we increased the threshold on the confidence that is used by the PT classifier on English to 0.9, the number of paraphrases reported by the inference algorithm decreased to 330 and the average number of similar words in a pair, increased.

s of that, the overall precision got improved to 72%, calculated over 250 evaluated pairs.


The opportunity to capitalize on this new market has encouraged internet service providers to provide web content in multiple languages.

the Web has become an attractive resource for acquiring structured and unstructured data across several low-resource languages and domains.


Similarly, the coreference annotation (and named entity annotation, too) operates directly on the text, not taking the underlying phrase structure into account.

units that enter the coreference relations sometimes do not correspond to a continuous subtree of a tree.


Therefore, units that enter the coreference relations sometimes do not correspond to a continuous subtree of a tree.

it is substantially non-trivial to search for phenomena that involve several such layers (e.g.


We believed that clause boundaries could be identified automatically with very high reliability, since gold-standard morphological and more importantly, analytical annotation had already been available.

clause boundaries were annotated manually only in a small portion of the PDT data and this annotation was used for developing a rule-based clause-identification procedure.


Endangered languages represent an especially urgent type of low-resource languages: Not only do they generally lack computational resources, but they also unfortunately have the property that the window in which to create such resources is small and closing.

to the extent that any computational resources are created, they are particularly valuable, and if they can be repurposed for applications beyond their original target, this extends their value further.


This results in imported lexical entries for 4,741 common nouns, 282 native Chintang intransitive verbs, 142 borrowed Nepali intransitive verbs, 285 native Chintang transitive verbs, and 190 borrowed Nepali transitive verbs.

5,640/9,034 (62%) of the entries in the Toolbox lexicon resulted in entries for the Matrix grammar, including 899/1,440 (62%) of the verbs.


We consider two different kinds of contexts: co-occurrence as immediate neighbors in a sequence, and co-occurrence within a sentence (sequences as limited by BoS and EoS).

for each corpus of text, composed of sentences, we can compute the co-occurrence graph by connecting word nodes with edges, if words co-occur.


It is quite reasonable to say, the larger n is, the closer the n-gram model is to naturalness.

the conjecture is affirmatively substantiated within the scope of our computational studies, if we find at least one network parameter whose values for the n-gram models show a strictly monotonous convergence behavior towards the value for natural language, and this behavior is consistent throughout all languages.


In fact, synonyms usually do not co-occur, but they share a large number of significant co-occurrences -an observation that leads to the operationalization of the distributional hypothesis (Miller and Charles, 1991).

when two such concepts are mentioned together frequently since they belong to the same topic, this leads to box motifs, as the following examples from the English real language network illustrate: we observe different kinds of word pairs for the same concept: synonyms like (award, price), same word stem within or across word classes like (winning, won) or (Greek, Greece), and artifacts of punctuation or spelling (ancient, Ancient) or (interview, "), box motifs capture a very loose notion of synonymy: "interview" and the double quote " e.g.


The other two baselines that achieved a better performance than our model are unigrams (U) for the t+I+R setting and unigrams+POS (U+POS) for the t+I+R setting with an F-1 measure of 0.691 and 0.69 respectively.

we see that we achieve classification performance which is similar to, and, in most cases, better than that obtained from the baseline features by using thread specific features which are much less in number (no.


A naive assumption about possible words is that they can be any arbitrary sequence of segments.

both dog and qfx would be equally good candidates for possible words a priori.


The collocation models assume that sentences are sequences of phrases that, themselves, are made up of individual words.

the model not only learns words but an additional kind of entity, entire chunks of words.


To understand why this isn't happening for the Unigram model, recall that under the Unigram model, all tokens in a hypothesis are fully independent whereas the collocation and the Bigram model assume dependencies between tokens.

under the Unigram model a solution involving a large number of items is automatically discouraged because it can only work with marginal probabilities, whereas the other models without syllable structure can "abuse" their respective notions of context to capture dependencies not only between words but also between subword units.


In this evaluation, we used the probabilities supplied by HPYLM+c without normalising over the finite vocabulary .

a comparison to baseline models that have a finite domain is somewhat biased in their favour.


For instance, we found only 26 manual lexical substitutions in the Simplext corpus, and our system only offered a lexical substitute for 12 of these manual substitutions.

53.84% of the manually substituted target words were not simplified by LexSiS, and 78.57% of these manual substitutions were found to be simpler synonyms by the annotators (see Table 4).


Instead of focusing solely on the dialogue, we must also focus on the dialogue participants and how their social aspirations constrain their dialogue.

we must look at the social intentional structure of dialogue which models the social intentions and goals of the dialogue participants and how the participants perceive the social intentions of others.


Complex predicates (CPs) are encountered frequently in general language use, as well as in newspaper corpora.

any NLP application, whether shallow or deep, whether its goal be parsing, generation, question-answering or the construction of lexical resources like WordNet (cf.


Further errors were introduced by punctuation and tokenization or white-space issues (see section 3.4).

the initial bigram lists, which consisted of 16033 possible combinations, were pruned.


We tokenized the essays and removed all punctuation marks, special characters and capitalization.

each essay is represented as an array of lower-case words.


In this study, the accuracy benefited from n-grams up to n = 5.

n-grams with n > 3, which are generally not considered in the related research, are not a priori useless.


In contrast, in a setup with a random baseline of 33.33% we obtained a best result of 95% -96.48% (depending on the employed n-gram class) on single-corpus evaluation using ICLE, and 86.24% -87.57% in a cross-corpus evaluation setup with training on ICLE and testing on NOCE/USE/HKUST (see Table 4 and Figure 6).

when using ICLE for training and another corpus instead of ICLE for testing, there is a drop of about 54% -77% in Brooke and Hirst (2011) but only around 9% in our work.


In both cases there were more than 100 different topics, and none of them matched between ICLE used for training and NOCE/USE/HKUST used for testing in the cross-corpus setup.

topic overlaps seem very unlikely to have notably skewed the results in our cross-corpus evaluation.


Each global feature is either the Pointwise mutual information (PMI) or normalized Google distance (NGD) of a pair of concepts in the set, calculated in terms of the sets of concepts that either (1) link to each concept in the pair, (2) are linked to from each concept in the pair, or (3) are in the intersection of the sets defined in (1) and (2), for each concept in the pair 10 .

gLOW attempts to solve the following optimization problem for a given document d: Where Γ * is the optimal output.


As an example, PropBank (PB) (Kingsbury and Palmer, 2002) made it possible to design accurate automatic Semantic Role Labeling (SRL) systems (Hacioglu et al., 2003).

we get the feeling that an application of SRL as a redundancy measure might suit well, since the textual similarity between a pair of sentences relies on a deep understanding of the semantics of both.


With the significant amount of information included in supertags, comparing two sequences of supertags is similar to comparing two syntax trees.

we require a deletion to retain the same sequence of supertags as that of the original sentence in order to ensure grammaticality.


The modifier entropy of N 2 is H(N 2 ) = −((0.9 log 0.9) + (0.1 log 0.1)) = 0.5.

we can conclude that the modifier of N 1 is more unpredictable than that of N 2 .


However, pleonasms are not general enough so might not be found frequently in text, which diminishes the amount of secret information which can be embedded in the text.

we collect more positive and negative data which are more frequent in text for training, developing and testing the n-gram count method and the SVM model.


In addition, as described in Section 2, the proposed secret embedding algorithm sometimes involves choosing a permutation from a group of candidates that all encode the same secret bit(s).

it is crucial to develop a method that can distinguish acceptable permutations from those having awkward wordings in a word ordering-based stegosystem.


Since the inter-annotator agreement on the collected human judgements is not 100%, as shown in Section 3.2, it is not clear whether the 90% precision achieved by the classifier really means that the remaining 10% sentences (false positives) would be viewed as suspicious in a real steganography setting.

we consider 90% to be a high level of precision/security.


The problem of using a hash function is that the generation of a desired bitstring cannot be guaranteed.

error correction codes must be used in this protocol when there is no feasible hash code available, which increase the size of the transmission data.


For example, if a document is about a fire accident, most of the annotated triggers in the gold standard are of type DIE.

knowing the document theme may help to identify triggers.


An SMT system performs worse when translating segments which are rare in the background model.

in the first step of our framework, we identify the in-domain segments in an input source text for the next simplification step.


Based on the word alignments, the simplified term "hypertension" can be separated from its context "suffered from" without violating the consistency.

we can successfully restore the original form "crystal induced arthritis".


Only monolingual corpus can be obtained in this domain, and obtaining parallel text by manual translation incurs high cost due to the involvements of domain experts (e.g., doctors).

we collect the bilingual syntactic units by taking a semi-automatic approach in order to make the best of manual efforts.


"Business" and "Education" frequently broaden to various sub-topics.

the messages retrieved by these types of queries are not internal coherent, at least not as strong as others' categories, even they are assumed to belong to the same category.


On the other hand, while the average testing document length for Chinese and Japanese is very short, we trained the algorithms with far more documents for these languages, and so the classification accuracies did not suffer.

we can see a tradeoff between the amount of training data and the average length of the documents being classified.


In Figure 6, we see that when we normalize for the number of unique tokens, the hitting 2-gram model achieves far greater accuracy than the other models.

for faster classification speed, reduced memory consumption, and lower quality training data, the hitting 2-gram is the way to go.


For example, such words as evil or villain are not applicable to all product domains.

we suppose that gathering a specialized sentiment lexicon for the product meta-domain can be useful for researchers and practitioners.


If there was a disagreement about the sentiment of a specific word, the collective judgment after discussion was used as a final ground truth.

of our assessment procedure we had obtained the list of 4079 sentiment words in the movie domain.


Precision@1000 in these lists was 26.9% and 35.5% accordingly.

our algorithms gave significant improvements over the baselines.


For domain specific and general sentiment lexicons we explored different word quantity thresholds: {1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000} and report the results with each of them (see Figure 2 and 3).

we denote by ) , , ( we can define the main measure in the current experiment: This is the difference between the accuracy obtained with the lexicon L and baseline lexicon FL, during the transfer from source domain S to target domain T. we also use the averaged variant of this measure: In our case 9  D .


For this task, the positive class is not the presence of a comma but rather an error of the writer's that involves a comma.

it is necessary to compare what the writer has written to an annotator's judgment, and only if there is a mismatch between the two do we have an error (the positive class); when writer and annotator agree, the case is a non-error (the negative class).


Until recently , error-annotated learner corpora were not generally available for use in system testing.

systems that were developed by different researchers were tested on different datasets having different error rates (Leacock et al., 2010).


We exploit Japanese as a pivot language, or more precisely terms written in Kanjis, to help decompose the terms of other languages into morphs and associate them with the corresponding Kanjis, in a fully automatic way.

kanjis play the role of a semantic representation for morphs.


As it was previously explained, our morphological decomposition technique relies on the alignment of terms with their translation in a pivot language (Japanese, Kanjis).

this approach makes a strong parallelism assumption: the term in Kanjis must be built in the same way than the one in the studied language.


The main problem is that we do not have examples of morphs that are known a priori to be related (like dermato and dermo in the previous example).

we use a simple bootstrapping technique: if two morphs are stored in γ as possible translations of the same Kanji sequence, and if these two morphs share a sub-string longer than a certain threshold, then we assume that they both belong to the same morpheme.


The more a rule is found, the more certain it will be.

we keep all the analogical rules generated at each iteration along with their number of occurrence, and we only apply the most frequently found ones.


The goal is of course to be able to associate a query containing stomachalgia with a document containing gastrodynia.

when indexing the collection, terms are decomposed.


WordsEye currently focuses on directly expressed spatial relations and other graphically realizable properties.

users must describe scenes in somewhat stilted language.


However, 'm was not recognized as a form of the word am-itself a form of the verb to be-but rather was analyzed as a distinct verb.

the extractor was modified to detect and fix this particular case.


Upon closer inspection of the source of the inefficiency, it became quickly apparent that the observed performance problems are the result of a conspiracy of several factors, most of which can be subsumed under the notion of non-configurationality: • Relatively free constituent order In contrast to English, constituent order in German clauses is relatively free, permitting permutation of complements, including the subject, as well as interspersal of modifiers in pretty much any position.

chart size grows rather quickly, with ambiguity packing being ineffective until rather large, i.e.


Since the core sentence has to be generated before it combines with the fronted element, construction of the core sentence needs to proceed without any access to valence information.

we experience a massive combinatorial explosion that can only be controlled very late, i.e.


In a non-configurational language such as German, which features argument composition, heads combine with complements that are not their own arguments but rather those of a predicate they compose with, i.e., they need to cater for unknown raised arguments by means of underspecified valence lists.

the identity of the inherited arguments is not known, so any XP present in the chart, however inflected, can sneak into these underspecified lists, to be ruled out, in the majority of cases, only when a significantly larger structure has been built.


Fortunately, whether some predicate permits argument composition or not is a lexical matter, with composition being restricted to auxiliaries, modals, raising and control verbs.

the transfer grammar marks the arguments of nonfinite complements of modals etc., as to their potential of undergoing argument composition (coh +).


Both local head-complement rules (na_or_−) and complement extraction rules (na_or_+) are made sensitive to this distinction.

of this feature, we expect some considerable reduction in chart size: with the exception of relativised arguments, all arguments will be marked as either local or non-local, thereby eliminating a great deal of non-determinism.


SRL does a better job at concept level parsing using predicate logic, but is bound by the strict predicate grammar.

although techniques such as NER and SRL is core to NLP, there is an inherent gap between requirements of intelligent tasks (like QA) and several state-of-the-art NLP techniques (Finkel, 2009).


The primary constraint R is a restriction on the form values that can be probabilistically assigned to W. Hence, W can take up values of different forms from the set (unit, class, … , transform) with probabilities ( , , … ) respectively.

w is constrained using the probability distribution R as: The singular variable w takes values from the universe of discourse U, such that values of w are singletons in U.


(Table 2), 'various TV' and 'movie adaptations' are connected by the conjunction 'and'.

we search in and since we find a sequence of two units ('Secret', 'Garden'), we collapse it to a single unit and represent it as root.


Disagreements were limited to just 1 node position in 82% disagreed cases.

we consider this hand labelled set as the gold standard for comparing the machine generated subnet.


Indeed, Baker (1996) observed that translated texts tend to bear features like explicitation, simplification, normalization and levelling out.

one of the difficulties with comparable corpora is that the translation of a source term may not be present in its "normalized" or "canonical" form but rather in the form of a morphological or paraphrastic variant (e.g.


The system generated the reference translation mastektomie but also translations like ablation der brust, abschnitt der brust, brustentfernun, entfernung der brust which are all exact translations.

if we take into account these translations, we find that 94% and 91% of the source terms had at least one exact translation for English to French and English to German respectively.


Among them, term frequency bursty is most correlated to topic evolution according to our analysis.

we segment the sub-topics by detecting term frequency bursty.


It is hard to label the influence of users, or to collect such information automatically.

in this paper, we evaluate the end-to-end contribution of social influence under the whole summarization framework.


Adjacent sub-topics are required to focus on different subjects, which make it a special text clustering task.

we treat each sub-topic as a cluster and automatically compute the average cosine similarity between adjacent ones as an indicator of segmentation performance.


By using machine learning terminology, the vocabulary prediction task can be categorized as a binary classification task: given a word and a learner, it predicts whether or not the learner knows the word.

a number of machine learning methods, such as a support vector machine (SVM) for the binary classification task, can be used as predictors.


Here, d v maps each word v into a point on the axis, and a u works as a threshold.

when P( y = 1|u, v) ≥ 0.5, we can assume learner u knows word v. Due to the logistic sigmoid function, P( y = 1|u, v) ≥ 0.5 holds true if and only if the Rasch model determines that learner u knows all words whose word difficulty d v is lower than the learners' ability a u .


There is a 1-to-1 mapping between parameters and words in this design of f .

if some words are missing in the training set, parameters arise that are not trained.


In Figure 3 (a), all three learners share the same word difficulty.

the model cannot represent a learner who knows the word "worship" but does not know the word "tremble".


With the general model explained above, we can easily explain the fundamental cause of this problem: in the current models, f (u, v) depends only on v, and does not depend on u.

tackling this problem is simple: let f (u, v) depend on u as well.


As we explained, the shared difficulty and Rasch models are special cases of the proposed model.

we first explain the optimization of the proposed model.


This function l W, a, w 0 is convex (Kajino et al., 2012) over all the variables W, a, w 0 .

the MAP model parametersŴ,â, andŵ 0 can be estimated by minimizing l W, a, w 0 w.r.t.


This dataset was created in Japan in January 2009 when Twitter was not as predominant as it is today.

some low-level learners knew the word "twitter" through the name of the service while some high-level learners did not.


For example, in the case of the word "mantle", the corresponding loanword in Japanese, the native language for most of learners of the dataset used, is spelled as "mantoru".

the difficulty has little influence on whether or not learners know the word in this case.


Rather, whether or not the learner can perceive the loanword in spite of spelling difference has more influence.

even low-ability learners can  perceive the meaning of the word through its corresponding loanword in L1, which makes the variance larger.


In this dataset, 1, 408 of 11, 999 words had the highest y variance.

y variance does not produce any interesting results.


In this case, n ≥ p + m and p ≤ m − 1, subtracting for the one upper bound that all of the K i have in common.

n ≥ p + m ≥ 2p + 1 and consequently p ≤ n−1 2 .


The LKB is approximately 1.82 times slower than ALE 4.0, and the experimental version implemented for this study is approximately 1.054 times slower than ALE 4.0.

the experimental system produces a run-time parser that is 5.4% slower than ALE after having compiled out the Dedekind-MacNeille completion in advance, but is still more than 40% faster than the LKB, a commonly used system that caches them on-line.


In our experiments both parsers utilize the same (training) data set -having two different representations -without having access to external information.

our results indicate that state-of-the-art parsers can be still further improved without additional data and that there may be approaches that reach even higher accuracy levels in a single pass.


ConstRel features are similar to POSRel but use the constituent labels rather than the POS tags of the heads.

for A we have VP-NP, VP-NP-OBJ (from invite-all), VP-PP, VP-PP-LOC (from invite-to).


(2008) present a theoretical analysis about argument positions and suggest that an argument should c-command a predicate.

our sequentialization precedure keeps most semantic roles.


At a finite temperature, the probability mass is split and given to all states, instead of a single one that minimizes the cost function.

most of the spin averages are hardly biased.


In particular, when J i j ≥ 0 holds for all spin pairs, which is the case for models of ferromagnetism, every element of the critical eigenvector x 1 has an identical sign.

the non-trivial solution is characterized by a non-vanishing value of magnetization: whose absolute value is kept non-negligible as N becomes large if and only if m i (i = 1, 2, .


As we speculated, the ferromagnetic phase transition was prevented, as shown by the magnetization for "all words" (squares) in Figure 3.

classification accuracy was improved beyond β ≃ 1.0.


The localized eigenvectors have non-negligible values only for a few elements.

removal of one of such components does not provide significant effects for most spins.


As shown in Figure 5, the eigenvectors of large eigenvalues, which are emphasized at low temperature, are mostly localized and could contain relevant classification information only for a few words that corresponds to non-negligible values of elements.

they are individually insufficient for correctly classifying most other words corresponding to negligible elements.


Moreover, comments on sociopolitical articles need not elaborate on all the issues in the article.

products and their features are not relevant to our problem.


Sometimes, one service is available locally as a plug-in to the server site.

the Sanskrit Heritage segmenter is available as a plug-in to the University of Hyderabad Sanskrit computational platform, in order to undo sandhi in sandhied corpus.


Clearly, the matching can not be done simply based upon the name of the stem, but the concepts involved in the corresponding entry also need to be used.

the headword linking problem is not trivial because of 1).


Homophony indexes: SH and MW have their own systems of giving homophony indexes to the entries.

aṅga 1 in SH may correspond to either aṅga 1 or aṅga 2 in MW, and 2).


(ps,dh) denotes the verb with a preverb sequence 'ps' and the root 'dh'.

a verb 'ānī', consisting of preverb 'ā' and root 'nī' was labeled asānī-cpvb.(ā,nī).


Fraser and Marcu (2007) study how alignment quality (AER) is related to its translation quality relative BLEU.

they proposed a modified version of AER to increase the correlation between alignment quality and translation performance.


In our specific application, we are interested in predicting translation quality.

y represents a translation quality score.


In this study, we are interested in the characteristics of the part translation model M that is visible to the decoder when translating.

for each input document s i we extract features from a submodel M i s.t.


Given the reduced amount of data we had (252 training instances), we opted to use leave-oneout cross validation instead of having separate training and test sets.

we trained our models in all-but-one instances, and used the remaining one for testing purposes.


While the focus of this paper is the analysis of the most important features for translation quality, which in itself is an important goal, the application of the acquired knowledge is essential for the improvement of translation systems.

below we provide a set of pointers on how to address specific issues.


The remaining cities form our city-based representation of geolocations.

of these procedures, Boston ends up as a single city (incorporating Revere and Chelsea), but neighbouring Manchester is a discrete city (incorporating Bedford) because it is in New Hampshire.


The key idea is that ME features connect a word and a class, with larger λ k weights indicating stronger word-class associations.

we should be able to use the learned weights as a means of both ranking features and grouping features by cities.


First, if z and z ′ have different length t = t ′ , let K walk (z, z ′ ) = 0.

for two walks with the same length t = t ′ , we define K walk (z, z ′ ) is the product of the similarity of the pairs of nodes and edges visited along the two walks z and z ′ .


, L. For t = 0, there exists only one walk pair of length 0, namely, z (0) = (w), z ′(0) = (w ′ ).

we have where the second equality follows from Eq.


The average value of L for each part-of-speech was L = 3.6 for noun, L = 2.5 for verb, and L = 4.2 for adjective.

in all cases, we have L > 1, so we conclude that it is worth considering structural co-occurrence of multiple words, not just single words that are directly connected to target words (which corresponds to L = 1).


However, if we do so, we again suffer from the problem of word-based methods (i.e., we will not generate grammatical compressions).

we select a set of bunsetsu, each containing unimportant words, and relax their unit constraints.


For example, if x i = 0 and x Last(b j ) = 1, the term penalizes the objective function.

the proposed method basically treats words in b j as a unit similarly to bunsetsu-based methods.


Since bunsetsu-based methods are limited by unit constraints, they have to treat each bunsetsu as a unit.

even if there are unimportant words in a bunsetsu, the methods do not drop those words from the bunsetsu.


On the other hand, our method relaxes unit constraints using Lagrangian relaxation.

our method has the ability to drop unimportant words from a bunsetsu, even though it is loosely based on bunsetsu-based methods.


As seen in the first experiment, dependency constraints were also effective in preserving important information.

we can say that there is no reason for not using the constraints in sentence compression.


In this example, the source sentence comprises 52 characters.

each method was required to produce a compressed sentence whose length was not more than 36 characters (52 × 0.7 = 36.4).


In particular, for document d 1 , we should rank higher the sentences that are more relevant than the snippet of d 2 : In our implementation, d ′ is a subset of d 1 composed by the sentences that contain at least one non-stop query term.

as such, we have a preference dataset about d 1 : the gradient training set for the sentence scoring function can be defined as: where x 1 and x 2 are feature vectors for s 1 and s 2 respectively.


However, since there are many ENE types and we need a reasonable amount of training data for each ENE type to avoid data sparseness, the manual creation of training data will be very costly.

to facilitate the creation of training data, our basic idea is to turn to an existing corpus annotated with ENE types.


For example, "London" is written with four consecutive Katakana characters in Japanese.

we have "K-K-K-K" (K stands for Katakana) as a binary feature.


We use a backward selection method to find the best combination of features; that is, we first use all the features and then remove one feature at a time whose removal improves the performance the most.

we removed seven features: (T3) Character unigram, (T4) Character bigram, (T5) POS unigram, (T10) Last two characters, (T13) Proper noun semantic categories, (T16) Character type construction, and (T7) Infobox attributes.


The granularity of the semantic prototypes was chosen to match linguistic usefulness, rather than for its descriptive value as such.

tags should optimally be distinguishable with linguistic tests, such as the combinatorial potential, e.g.


For simplicity, this method represents each object with a vector of two sets of features, and this kind of representation would inevitably result in information loss to a certain extent.

we plan to try better alternatives such as hypergraph or tensor model, and make effort to improve the existing work on content or social network-based user interest modeling.


On one hand, tweets from different sources tend to contain non-informative noise such as subjective comments and conversations.

it is challenging to identify salient information from tweet content alone.


In these situations, users are likely to be unaware of each other.

they fail to connect with many others on topics of mutual interest.


However, building large and wide-coverage treebanks for CCG is expensive and time-consuming.

how to induce CCG lexicons and grammars from unlabeled sentences has great values.


In addition, the first approach may contain "maximal" information gathered from the whole dependency graph that is not available during transitions of the conventional parser (Attardi and Ciaramita, 2008).

we use the first approach to generate graph sequences.


Otherwise, after appending g to the tail of each d i , denoted by 〈d i ◊g〉, 〈d i ◊g〉 is added to DB similarly to the case in line a7.


Unfortunately, the corresponding resolvers were either not evaluated in large-scale challenges or did not show convincing performance in the challenges.

the question remains open whether employing world knowledge can improve coreference resolution in large unfiltered corpora.


The ILP objective function is as follows.

the cost of H is the sum of the costs of p ∈ P, such that p is included in the hypothesis (h p = 1) and is not explained (r p = 0).


For example, d o g(x) ∧ animal( y) will be annotated with x = y, but not with d o g( y).

we modified the original algorithm for learning weights from a partial gold standard.


An n-gram fragment will obtain a high cosine similarity if it is close to the document vector in the LSI latent space and a low cosine similarity value if it is not close.

what we expect is that domain-specific n-grams will obtain a high cosine similarity (Jameel et al., 2012) value compared with common/general n-gram fragments.


The optimal cost for all the terms from t 1 until position t i , (denoted as C 1 (T i )) can be written as: The final reading difficulty of a document will not only depend on cohesion but also specificity.

to compute the final readability cost of a text document, we linearly combine specificity values of the n-grams formed during sequential linear n-gram determination scheme using Equation 5.


Selecting a higher value of k would lead to a huge cognitive load on the human subjects (which we describe later in the text) during annotation.


We extend the "Best-Link" strategy by including several constraints in its objective function as shown below.

given a document d and a pairwise coreference scoring function pc that maps an ordered pair of mentions to a value indicating the probability that they are coreferential, we generate a coreference graph g d according to the following decision model: For each mention m i in document d, let B m i be the set of mentions appearing before m i in d. B m i = {m 1 , m 2 , ..., m i−1 }.


However, the difference in such cases is not statistically significant (69.1 vs 69.3 and 58.3 vs 58.4).

we see that both, knowledge and constraints, benefit Problem mentions.


During a conversation, a human with knowledge of the facts might not only say that the utterance (2) is wrong, but respond with something like "not Germany, but France."

we believe this kind of validation system we are proposing here may have application outside of a strict question-answering framework.


Rather than the implementation of fully automatic system, this study focuses on the methodologies and the headroom analysis of this fact validation.

the conversion of questions is done manually, but it is conducted by using specific algorithms and can be automated using standard techniques and resources.


The proposed method took into account familiarity and homophones of kanjis.

of human evaluation, we confirmed that distinctive explanations generated by our system outperformed those in existing screen readers.


One important point to mention is that we only included positive sentences (sentences with at least one phenotype) in our training set, because the number of negative sentences was far greater than positive sentences and it prevented the system from training efficiently.

whenever new phenotype names were found, the number of sentences in the training set increased for the next iteration.


CCG categories encoded detailed subcategorization information that is not present in DG labels, and the restrictions in combinator application constrain the way CCG derivations can be built, whereas dependency arcs may appear between any pair of words under a DG.

we expect that CCG and DG analyses will provide markedly different insights, despite both producing dependency-style output.


The rate of the update for each task is defined by a K × K interaction matrix A, which, intuitively, encodes relatedness between the tasks.

when a learner makes a mistake, the compound weight vector update rule applied is where ⊗ is the Kronecker product and I m is the identity matrix of size m. This update can be rewritten as separate updates for each task: This learning algorithm directly corresponds to the minimization of the following objective: where is the hinge loss on the example at time t. this setup can be naturally extended to other loss function and to non-linear models.


However, if m w were 1 and w were not aligned to other words, the self update rateÂ −1 w,w would be twice as large.

the +1 term reduces the effect of potentially noisy counts.


Obviously, various arguments closely related with an event trigger contain necessary semantic information for event pronoun resolution.

it is reasonable to represent an event mention using the event trigger and corresponding event arguments.


This method, though sounds good, practically ends up generating thousands of splits 90% of which are not validated morphologically.

this method is computationally less efficient.


The problem of constituency parsing is similar to the problem of completely parenthesizing n+1 factors in all possible ways.

the total possible ways of parsing a compound with n + 1 constituents is equal to a Catalan number, C n (Huet, 2009).


In other words, the classification is not guided by the deeper semantics, but by the paraphrase of a given compound, or by what the language expresses.

on the one hand, to decide the meaning of a compound, we need a fine-grain tagset, at the same time, it should not be as fine-grained as to distinguish between the meaning of genitive cases in rājñah .


the association score of o and s i is defined as:  where distance(o, s i ) is the dependency distance (see Section 3.4 for the details) between o and s i ; and μ is the parameter for decay rate determined by maximum likelihood estimate: where ) ( k p is the probability of the distance between o and s i being k. Since our goal is disambiguating opinion words in opinionated sentences, we should give product-feature words more influence on the translation selection than normal words.


Although the box-like representation captures the DRS structure very well, it does not provide us with a way to freely break and combine components.

we represent a DRS via a semantic graph, which is simple and directed (see Figure 1c).


Indeed, semantic graphs provide us with maximal freedom in breaking components.

learning a semantic lexicon becomes easier.


As discussed above, the quality of the alignment process strongly affects the final performance of DeSCoG.

in this section, we will look into it in some more details.


In order to calculate Z for each G c , B, we need to take the sum over all feasible W 's, which is intractable.

z is approximated on M -best W 's z ≅ M i=1 e F (W i ) −1 .


People with commercial intentions target larger audiences and do not bother about minority languages spoken by communities that speak some other major language.

the language filter does the job perfectly.


This sometimes can lead to poor quality translations because translation context is limited by the value of n. In contrast, example-based MT (EBMT) usually stores the full sentence pairs in source and target data in an example base and uses translations of sentences similar to the input sentence as a template for translation.

eBMT systems can often better capture long range dependencies and rich morphology.


First, they try bucketing sentences by length to limit the number of sentences in the example base that have to be compared against the input, assuming that similar sentences will have similar length.

the best match is not guaranteed to be found when this heuristics is used.


we explore how to optimize the IR settings for the ASM problem.


The use of data structures such as ternary search trees and tries is more efficient, but requires that the structures are kept in memory.

these approaches have higher memory requirements.


In practice, this implies that the user of an EBMT system can control whether he is more interested in a high-quality or in a fast translation.

it proves important to choose source-side sentences with care.


Rather than labeling 'Y' or 'N' for every character in standard words, they give each character the following 4 types of label: (1) one of the 0-9 digits; (2) one of the 26 characters; (3) null character '_', and (4) letter combination (they used 5 common combinations, such as 'wh', 'ck', and 'ey').

this system can handle other types of non-standard tokens, such as substitution.


As described earlier, in order to extract phonetic features for character-block sequence labeling model, we only use the words that have the same number of character-block segments and phones.

the number of the final standard words used to create the lookup table is 10,105.


For model inference, we use a straightforward Gibbs sampler based on the Chinese Restaurant Franchise (CRF) and the stick-breaking construction.

we begin with an analog of the CRF process for h-uHDP: a document   given the rest of the variables is: , ,   where e m  denotes the total number of tables in epoch e and { , } given a specified aspect k:   All the counts above except    , ,  x is assigned to aspect k. The base measure G 0 was set a symmetric Dirichlet distribution with parameters  (e.g.


(2012) is that they extract all single-character verbs contained in triggers as BVs (e.g., "担" (undertake, verb) and "任" (serve as, verb) are treated as two BVs for "担任" (serve as)).


身亡" (die)) has the Subject-Predicate structure.


Besides those un-annotated true trigger mentions, which much encumber the performance of trigger identification and overall event extraction, we find that 9.7% of the pseudo trigger mentions in the ACE 2005 Chinese corpus are actually true ones.

a natural extension of this work is to explore some effective methods to recover those pseudo-annotated true trigger mentions.


However, it is usually a single-character word which means "sour" in general text, and should be tagged as "S".

those surface features (such as character n-grams) adopted in the approaches mentioned above have more difficulty in identifying the correct position-tag, which is a member of {Beginning, Middle, End, Singleton} (Xue, 2003), of a character within an OOV word in this case.


However, the above dictionary related feature utilized in discriminative approaches (Low et al., 2005;) cannot be directly adopted by a generative model 3 .

we propose a new tag-matching-status feature for checking if the selected position-tag matches the longest dictionary-matching-word, and derive a novel enhanced character-based generative model.


a unit is either a Chinese character or a foreign/numerical expression as defined above, and is represented by either a Chinese-Character-ID or a Meta-Type-ID (i.e., FN-type or CN-type) in the model.


It is observed that tag matching status is less reliable when there are several dictionary matching words for the given character.

the character context is introduced to help disambiguate them.


However, G2 (with context) also boosts the performance for seen cases.

it will not let the newly added tag-matching factor contaminate the original trigram model when associated character-tag bigrams are seen.


Since the external dictionary is expected to be collected by the user in real applications, dictionary words should be consistent with his/her own segmentation criterion.

to give true evaluation for reflecting the real situation, words in the dictionary are first transformed into their corresponding ones according to the same criteria adopted in various given corpora.


Among those 922 OOV errors, 758 (82.2%) of them are not covered by the dictionary and 401 (52.9%) out of 758 are technical terms.

it again confirms how important a dictionary is.


However, this approach needs an additional module to recognize OOV words.

the character-based discriminative model (Xue, 2003;Low et al., 2005;Zhang et al., 2006;Jiang et al., 2008; has become the main stream due to its capability in handling OOV words.


Thanks to relatively richer morphologies, English POS tagging achieves much higher accuracy than Chinese (97% vs. 94%).

english dependency parsing suffers less error propagation problem than Chinese.


Under the joint model, the weights of POS and syntactic features, denoted by w pos⊕syn or w joint , are simultaneously learned.

they can interact with each other to determine an optimal joint result.


the weights of the POS features and the syntactic features are of the same scale after training is completed.


We find that in f joint (x ( j) ,t,d), f syn (x ( j) ,t,d) contains more than 3,000 non-zero features, whereas f pos (x ( j) ,t) only has less than 200 non-zero features on average.

the model is dominated by the syntactic features and the POS features would play a very limited role in determining the best joint result.


contains much less non-zero features than f syn (.

), f pos (x ( j) , t ( j) ) − f pos (x ( j) ,t) 2 is much smaller than f syn (x ( j) , t ( j) , τ pos is much larger than τ syn .


This means that the POS features are updated in much larger step than the syntactic features.

the POS features play a more important role in the joint models.


Obviously, SPA can raise the weight of the POS features, as the POS score is about 1/8 (10 0.9 = 8) of the syntactic score.

the POS features can make more contributions, and the discriminative power of the syntactic features in resolving the syntax-sensitive ambiguities and the power of the POS features in the syntax-insensitive POS ambiguities can be better balanced.


For the large volume of documents, it will be inefficient for human editors to manually index keyphrases.

automatically extracting keyphrases for documents is proposed as a challenging task in natural language processing.


This reveals that it is necessary to address the sparsity problem of TWTM by smoothing with WTM.

when comparing TWTM with other methods, we set λ = 0.4 for TWTM.